52 results about "Density functional theory" patented technology

The invention discloses a method and system for simulating the operating environment of a hydrogen energy reaction kettle, and the method comprises the steps: building a multi-scale simulation frame based on the hydrogen energy reaction kettle, and carrying out the modeling of a micro scale, a mesoscale and a macro scale of the hydrogen energy reaction kettle; performing multi-physical field coupling simulation on the hydrogen energy reaction kettle, including coupling calculation of a flow field, a temperature field, a concentration field and a stress field in an operation environment of the hydrogen energy reaction kettle; performing intelligent optimization simulation based on a multi-scale simulation framework and multi-physics field coupling simulation, wherein the intelligent optimization simulation comprises adaptive grid optimization, non-uniform inlet simulation and two-phase flow simulation; and constructing a digital twin platform, and carrying out system-level dynamic simulation and intelligent control on the hydrogen energy reaction kettle based on the digital twin platform. According to the method, all-around modeling and simulation from a micro scale to a macro scale are realized through a plurality of methods such as a density functional theory, molecular dynamics, a lattice Boltzmann method and computational fluid dynamics.

The invention discloses a simulation modeling method and device for a nanoscale device, relates to the technical field of semiconductor modeling and simulation, and aims to solve the problem that efficiency and precision cannot be considered in nanoscale device modeling and simulation in the prior art. The method comprises the steps that key material parameters are extracted from electronic structure basic data of a target nanoscale device, a default material parameter library in TCAD software is corrected based on the key material parameters, and a corrected material parameter library is obtained; electronic structure basic data is obtained based on a density functional theory; based on the corrected material parameter library, optimizing adjustable parameters of a ballistic transport model preset in TCAD software in combination with a transport IV curve to obtain a ballistic transport model after parameter optimization; the transportation IV curve is generated by adopting a quantum transportation method based on an unbalanced Green function in combination with key material parameters; and integrating the key material parameters with the ballistic transport model after parameter optimization, and constructing a simulation parameter library of the target nanoscale device.

The invention relates to a characterization method of a thermal decomposition mechanism of an azo initiator. The characterization method comprises the following steps: (1) carrying out thermal stability analysis on the azo initiator; (2) acquiring microscopic decomposition characteristic data of the azo initiator by using a thermogravimetric-Fourier transform infrared spectroscopy combined technology; (3) constructing a three-dimensional molecular structure model of the azo initiator; (4) calculating the bond dissociation energy of the key covalent bond based on the density functional theory; and (5) the thermal decomposition characteristic temperature, the heat release behavior, the decomposition product and the evolution path of the azo initiator are determined by integrating the experimental data measured by the combination technology and the bond dissociation energy obtained by theoretical calculation, and the complete thermal decomposition mechanism of the azo initiator is clarified. According to the method, the thermal decomposition rule of the azo initiator is disclosed comprehensively and accurately through a technical path of combining experimental analysis and theoretical simulation and verifying macroscopic characteristics and micromechanism, and a key theoretical basis and technical support are provided for preventing and controlling the thermal runaway risk of such substances.

The invention relates to the technical field of information communication particularly suitable for specific application fields, and discloses a machine learning-based ionic liquid screening method for catalyzing CO2 cycloaddition reaction. The method comprises the following steps: constructing an ionic liquid three-dimensional structure database; calculating and generating a three-dimensional electrostatic potential and electron density data field through a density functional theory; voxelizing the data field into a standardized multi-channel three-dimensional tensor; utilizing a three-dimensional convolutional neural network regression model to automatically learn space electron characteristics and predict catalytic activity; and performing high-throughput virtual screening on the candidate library. Through end-to-end learning of the mapping relation between the three-dimensional electronic structure and the catalytic activity of the ionic liquid, the prediction accuracy and the screening efficiency are improved.

The invention provides an iron-sulfur isotope equilibrium fractionation factor calculation method based on a density functional theory. The iron-sulfur isotope equilibrium fractionation factor calculation method comprises the following steps: firstly, constructing initial mineral models of amorphous FeS, cubic FeS, maquinol ore, hematite, hematite and pyrite; the model is optimized through a spin polarization DFT + U method, GGA-PBE functional, OTFG ultra-soft pseudo potential, 400 eV plane wave cutoff energy and 5.0 * 10 <-7 > Hartree self-consistent field convergence standards are selected as calculation parameters, and the optimal Hubbar Ueff value and magnetic sequence arrangement of each mineral are determined; and finally, calculating the simple harmonic vibration frequency of the mineral, and deducing equilibrium fractionation factors (1000ln beta value) of Fe and S isotopes at a specific temperature. According to the method, the theoretical blank of isotope fractionation of the conversion path from amorphous FeS to pyrite is filled up, the data can be used for judging the isotope balance of minerals and tracing the mineralization process, and an important theoretical basis is provided for research on sedimentary diagenesis and ancient ocean redox.

The invention discloses a high-performance biosensor based on a g-C3N4 / TiO2 heterojunction as well as a preparation method and application of the high-performance biosensor, and belongs to the technical field of biosensing and food safety detection. The sensor is constructed by taking a glassy carbon electrode as a substrate and sequentially modifying a g-C3N4 / TiO2 heterojunction composite material and diamine oxidase (DAO). The g-C3N4 / TiO2 heterojunction is prepared through a one-step calcination method, a type II p-n junction structure is formed, photo-generated charge separation and interface electron transfer are effectively promoted, and the conductivity, catalytic activity and enzyme immobilization capacity of the electrode are remarkably improved. The invention discloses a synergistic mechanism of heterojunction interface enhanced histamine selective adsorption and catalytic reaction through density functional theory calculation. The sensor shows excellent detection performance on histamine: the linearity is good in a range of 0.05-10 mM, and the detection limit is as low as 0.075 M; the selectivity is still higher than 93% in the presence of an interferent with ten times of concentration; when the method is applied to detection of actual samples of beef, mutton, fish and chicken, the adding standard recovery rate is 93.60%-101.8%, and the relative standard deviation is smaller than 5%. The sensor has the advantages of high sensitivity, good selectivity, strong anti-interference capability, good reproducibility and the like, and a reliable new method is provided for rapid and accurate screening of histamine in food.

The invention provides an LET microscopic confirmation method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductor data processing, and the method comprises the steps: setting initial conditions of a semiconductor material and charged particles; simulating the movement of the charged particles in the semiconductor material based on a time-dependent density functional theory to obtain a simulation result; and determining the LET according to the simulation result. The accurate setting of the initial condition provides a reliable input basis for the simulation process, and the TDDFT-based simulation process can truly restore the interaction mechanism of the charged particles and the semiconductor material from the microscopic level, thereby avoiding the defect of lack of electronic structure information in the calculation of the traditional empirical formula. And LET is determined through directional analysis of a simulation result, so that the accuracy and scientificity of LET calculation are remarkably improved, and powerful theoretical support is provided for engineering application such as radiation hardening design of semiconductor devices and research and development of radiation protection materials.

The application relates to the field of environmental science and technology, and discloses a fine-source analysis method for atmospheric pollutants combining computational chemistry and isotopes, which comprises the following steps: constructing an isotopic data set, simulating a target pollutant molecular configuration, constructing an isotopic fractionation paradigm equation, correcting local isotopic fractionation, tracing the generation mechanism of a target pollutant in a typical region, and optimizing source analysis of the target pollutant in the typical region. In this way, based on computational chemistry, the density functional theory and the U-B-GM model are coupled, the isotopic fractionation paradigm equation is constructed for a preset reaction path, the specific isotopic fractionation value or fractionation coefficient is accurately simulated in combination with atmospheric environmental parameters during observation, and a local isotopic fractionation correction scheme is established accordingly; the correction scheme is oriented towards fine-source analysis, and can accurately identify the generation mechanism and pollution sources. In this way, the uncertainty of source analysis can be effectively reduced, and fine tracing of the sources of atmospheric pollutants can be realized.

The invention provides a comprehensive molecular simulation optimization method for DMMP and derivatives thereof. The method comprises the following steps: S1, constructing an initial three-dimensional model of DMMP parent molecules and structural variants thereof; s2, performing conformation search on molecules of the component S1 to obtain a low-energy conformation; s3, aiming at the low-energy conformation in the S2, geometric structure optimization is carried out based on a density functional theory, and the stability of the optimized structure is verified through frequency calculation; s4, carrying out electronic structure analysis on the optimized structure passing the stability verification; s5, performing spectrum prediction on the optimized structure passing the stability verification; and S6, based on the results of S4 and S5, predicting the prospect of the structural variant as the flame retardant. According to the method, a reliable theoretical research idea is provided for researching a novel efficient flame retardant and designing a molecular structure by systematically researching the influence of the structural change on the physicochemical properties and flame-retardant efficiency of molecules.

The application discloses a kind of by density functional theory reveals the calculation method of micro-mechanism and selectivity source of palladium catalysis olefin amine 1,2-diarylation reaction, belong to the intersection technical field of organic synthesis and computational chemistry. Including constructing molecular structure;The configuration optimization and calculation are carried out to molecular structure, obtain thermodynamic data;The geometric configuration of intermediate and transition state in each step is calculated, the change of gibbs free energy is obtained, the connection relationship of transition state and corresponding reactant, product is determined;According to thermodynamic data and connection relationship, reaction path diagram is drawn, and reaction mechanism is determined;According to reaction mechanism, the step of determining the selectivity of reaction region, enantioselectivity and chemical selectivity is determined, and selectivity control strategy is analyzed and output. By simulation, the key intermediate and transition state structure of each element step of reaction is determined, the reaction energy barrier of each element step is quantified, the transition state action mechanism determining the selectivity of reaction region, enantioselectivity and chemical selectivity is analyzed in depth, to provide theoretical guidance for the design of high-efficiency catalyst and the optimization of reaction condition.

Provided are an electrode having particularly excellent atmospheric stability and electron injection properties, and an organic element using such an electrode. The present invention relates to a thin-film-modified electrode having a layer containing a metal and an organic compound capable of coordinating with the metal, or a layer having a structure in which a layer containing a metal and a layer containing an organic compound capable of coordinating with the metal are sequentially adjacent to each other on an electrode, the thin-film-modified electrode being characterized in that: the thin-film-modified electrode has a thin-film-modified layer having a structure in which a layer containing a metal and a layer containing an organic compound capable of coordinating with the metal are sequentially adjacent to each other; in calculation based on the density functional theory in which a metal is an aluminum atom, when the structure is optimized using a generic function: B3LYP / primary function: 6-31G, the sum of the distances between an atom coordinated with the metal and the aluminum atom in the organic compound is 4.06 or less.

This invention relates to the field of chemical MOF catalysis technology and discloses a method for high-throughput screening of MOF-catalyzed carbon dioxide cycloaddition catalysts. This method solves the technical challenge of achieving high-throughput material screening in the catalysis field of MOF material design through reasonable feature descriptor selection, reasonable machine learning algorithm selection, and evaluation model. Specifically, this invention utilizes a machine learning program based on density functional theory (DFT) calculations and low-cost Monte Carlo (GCMC) simulations to obtain MOF electronic and structural properties, combined with descriptors of reaction conditions, to establish a machine learning model. The TOF value of the catalyst is selected as the classification criterion to establish a high-throughput screening model. The high throughput in this process is mainly reflected in the reduced computational cost through reasonable descriptor selection, enabling large-scale screening while maintaining computational accuracy. Furthermore, the application of machine learning technology also provides a data analysis solution for MOF catalyst design.

This invention relates to the field of anthocyanin detection technology, specifically to a database of six anthocyanin ion pairs and its establishment method, and a method for identifying six anthocyanins in blueberries based on this database. Under ultra-high performance liquid chromatography / high resolution tandem mass spectrometry (UHPLC / HDMS) conditions, this invention collects the precise mass-to-charge ratios of the precursor ions of the six anthocyanins and the precise mass-to-charge ratios of all their corresponding product ions; using MS-FINDER software, it derives the possible chemical structures of the product ions; finally, it derives the determined chemical structures of the product ions according to DFT density functional theory, ultimately establishing a database of six anthocyanins ion pairs. The identification method of this invention is simple, mainly using ring-fracture ion pairs for judgment. Each of the six anthocyanins has different complementary ion pairs, which can be used for qualitative analysis. The ion pair database of this invention can serve as a basis for non-targeted detection, optimizing the detection steps, improving detection efficiency, and exhibiting high accuracy and sensitivity.

The application provides a first-principle-based X-ray Thomson scattering spectrum simulation method, which decomposes the scattering spectrum of a warm dense matter into an elastic part and an inelastic part based on quantum perturbation theory, wherein the elastic part is calculated by first-principle molecular dynamics and finite-temperature density functional theory, the inelastic part is calculated by a perturbation formula of time-dependent density functional theory, and the calculated elastic part and inelastic part are integrated to obtain a complete X-ray Thomson scattering spectrum of the warm dense matter. The method uses first principles to accurately simulate the internal environment of the warm dense matter, ensures that the finally simulated X-ray Thomson scattering spectrum truly reflects the internal environment of the warm dense matter, facilitates accurate spectrum decomposition operation on the actually measured scattering spectrum, and improves the research reliability of the warm dense matter.

The invention provides a method for designing a ferrite core based on multi-scale analog computation. According to the method, basic parameters of the ferrite material are collected, the electronic structure, the microstructure and the macroscopic magnetic performance of the ferrite material are subjected to analog calculation by combining a time-dependent density functional theory, molecular dynamics simulation and a multi-scale simulation technology of finite element analysis, the structure and components of the ferrite material and the shape structure of a ferrite device are optimized, and the performance of the ferrite device is improved. And the design of high-performance ferrite materials and devices is realized. According to the method, the material design efficiency is improved, the experiment cost is reduced, and the important application value is achieved.

This invention discloses a method for predicting the liquefaction temperature of a gas, comprising: obtaining an initial gas crystal structure; obtaining a stable molecular mechanical environment gas crystal structure based on density functional theory; obtaining a stable force field energy environment gas crystal structure using Compass II force field optimization; calculating the average interaction force and density of each single molecule in the two gas crystal structures respectively; comparing the average interaction force and density of the two gas crystal structures to obtain an initial liquefaction temperature prediction model; continuously adjusting the force field parameters of the initial gas crystal structure in the initial liquefaction temperature prediction model to update the initial liquefaction temperature prediction model to obtain the best-fitting liquefaction temperature prediction model; and incorporating the amorphous unit cells of multiple gas molecules to be tested into the best liquefaction temperature prediction model to obtain liquefaction temperature curves and determine the liquefaction temperature range. The gas liquefaction temperature prediction method disclosed in this invention can accurately predict the liquefaction temperature of a gas.

The invention discloses a high-lithium-affinity Cu2O material, a screening method and application, and relates to the technical field of electrode materials. Comprising the following steps: taking a copper source, an alkaline precipitator and a reducing agent as raw materials, adopting a liquid phase reduction method, inducing growth of different crystal faces by changing the concentration of a precipitator solution, preparing Cu2O materials with different crystal face orientations, adopting a density functional theory for simulation, calculating binding energy of different crystal faces of the prepared Cu2O materials and a single lithium atom, and calculating the lithium atom binding energy of the single lithium atom. The crystal face with the strongest binding energy is screened out, so that the Cu2O material with the high lithium affinity is obtained through screening; according to the method, growth of different crystal faces is induced by regulating and controlling the concentration of a precipitant solution, Cu2O materials with different crystal face orientations are prepared, the optimal crystal face orientation is screened out by combining theoretical calculation, and therefore the high-lithium-affinity Cu2O material is obtained through screening, and it is proved through electrochemical test empirical analysis that the screening method is correct and effective.

The invention discloses a construction method of a channel tight constraint model of an aperiodic semiconductor device with defects. According to the method, a supercell atomic structure is used as a feature, a density functional theory calculation energy band is used as a label, and a physical perception graph neural network integrated with a symmetric graph attention mechanism is constructed. In the feature learning and energy band fitting process, the model applies physical constraint of exponential decay to transition parameters. Through co-training defect-free and defect-containing supercells, the network can automatically analyze local perturbation caused by defects and dynamically adjust local parameters related to defect positions in a defect-free model so as to precisely fit an energy band structure of a defect system. According to the mechanism, the consistency of the super-intercellular transition matrixes of defect-containing and defect-free systems is strictly guaranteed while the model is constructed, so that a whole set of model parameters are output. According to the method, the technical bottleneck that a coupling matrix between complex systems is difficult to obtain in semiconductor material modeling is effectively solved.

The application discloses a modified asphalt coated carbon-based material optimization design method based on multi-scale simulation and belongs to the technical field of computational materials science and new energy battery materials. The application aims to overcome the defects of the traditional experience trial and error research and development mode, and constructs a whole-chain closed-loop calculation process of "molecular scale interface property preliminary screening - mesoscopic scale morphology and ratio optimization - reaction scale carbonization process regulation". The method cooperatively simulates molecular dynamics, density functional theory, dissipative particle dynamics and reaction force field molecular dynamics through cross-scale, and directionally outputs a material scheme with graphene oxide as a functional additive (the addition amount is 1%-5% of the mass of asphalt), and an optimal process window with a carbonization final temperature of 1000-1300 DEG C and a heating rate of 2-8 DEG C / min. The application can cooperatively improve the interface bonding strength, structural compactness and mechanical stability of the coating layer, and provides a predictable rational design for the development of high-performance alkali metal ion battery negative electrode materials.

The application discloses a high-vanadium-content V-Ti-Ni shape memory alloy and a preparation method thereof. The method is based on density functional theory simulation to screen the best composition of V-doped Ti-Ni alloy. Then, high-purity sponge titanium, nickel and self-made vanadium-nickel alloy are used as raw materials to smelt, hot forge, twice hot roll and cold draw in a vacuum induction furnace to obtain a high-vanadium-content V-Ti-Ni shape memory alloy wire with a wire diameter of Φ2-Φ3 mm. The hardness and elastic modulus of the final product, the high-vanadium-content V-Ti-Ni shape memory alloy wire, are equivalent to those of conventional Ti-Ni, and the martensitic phase transition temperature is significantly reduced, so that the reversible shape memory effect can be realized in an extremely low-temperature environment, and the high-vanadium-content V-Ti-Ni shape memory alloy wire is suitable for deep sea, deep space exploration and low-temperature micro-actuator manufacturing in a low-temperature environment. The preparation method can be directly applied to an existing Ti-Ni alloy production line without adding special equipment, and the process is economical and cost-effective.

A computer-readable recording medium stores therein an information processing program causing a computer to execute a process, the process including: obtaining one or more candidates for a setting value related to an execution environment for parallel processing of a density functional theory calculation for a substance; calculating a sum of costs for each of the obtained one or more candidates, the sum of costs being calculated based on one or more model expressions that, respectively, correspond to one or more calculation processes related to the density functional theory calculation and that, respectively, output an estimated value of a cost incurred when a corresponding one of the one or more calculation processes is performed in response to input of the setting value; and determining the setting value based on the sum calculated for the each of the obtained one or more candidates.

The invention relates to a microphase separation polymer glass transition temperature layering prediction method based on multi-source feature fusion, in particular to a machine learning prediction technology combining density functional theory calculation, thermodynamic solubility parameters and process formula parameters, and belongs to the technical field of artificial intelligence auxiliary material design and polymer physics. The prediction method comprises the following steps: aiming at a heterogeneous polymer material with a microphase separation characteristic, dividing an input characteristic into four physical layers, namely an intrinsic chemical layer, a microscopic electronic layer, a mesothermodynamic layer and a macroscopic process layer by adopting a layered characteristic engineering strategy; and establishing a mapping relationship between the hierarchical features and the glass transition temperature by using a machine learning model, so as to predict the glass transition temperature.

The present application relates to a kind of method based on density functional theory reveals the desulfurization mechanism of porous adsorbent Zn-MOF-74, using Multiwfn software calculation and experiment is combined, the method for researching the high-efficiency adsorption mechanism of Zn-MOF-74 porous adsorbent SO2, Zn-MOF-74 porous material prepared by using solvothermal method shows great superiority in desulfurization, using the quantum chemistry method based on density functional theory, at atomic molecular level by conceiving search, electrostatic potential, van der waals potential, binding energy, weak interaction force analysis, the calculation of molecular pore free volume, combined with experimental results, reveals the high-efficiency adsorption mechanism of Zn-MOF-74 porous material;The adsorption behavior of SO2 in the channel inside adsorbent is explained quantitatively and qualitatively in the form of image, and a theoretical basis for the application of quantum chemistry theory in the field of chemical technology is laid.

The application provides a data-driven magnetic alloy multi-objective optimization design method, which comprises the following steps: establishing a composition-performance historical data set; establishing an initial feature pool; performing feature selection on the initial feature pool to form an optimization feature pool; training a machine learning model to perform regression prediction on a target performance; setting a search space; performing preliminary screening on the search space; calculating the expected improvement value of the target performance; calculating the Pareto optimal solution; if the generated Pareto optimal solution result does not converge, performing calculation on the Pareto optimal solution based on the density functional theory to obtain the theoretical values of a plurality of property parameters as supplementary material characteristics and incorporate them into the feature pool; performing calculation on all compositions in the composition-performance historical data set to obtain supplementary material characteristics and incorporate them into the feature pool; and repeating the above steps until the generated Pareto optimal solution result converges to obtain alloy composition recommendation results.

A method and apparatus for generating data for training a machine-learning force field (MLFF) model are provided. The method of generating data for training the MLFF model includes generating product information based on received reactant information, generating labeled training data based on a density functional theory (DFT) calculation of the product information, identifying outlier data from verification data, based on an inference result of the verification data by the MLFF model trained based on the training data, and updating the training data based on the outlier data.

The objective is to improve the accuracy of total energy estimation by using neural network potential (NNP). One aspect of the present invention is a training device comprising a control unit for training an estimation model, which is a mathematical model for estimating the energy of atoms to be evaluated on the basis of feature quantities to be evaluated represented from first to u-th sums, wherein the u-th sum is the sum of: a three-dimensional wave function representing a u-th atomic orbit starting from an orbit having a maximum occupancy level as an atomic orbit of an atom to be evaluated located in a system comprising one or more atoms, a three-dimensional wave function of each u-th atomic orbit from an orbit having a maximum occupancy level with an atomic orbit of each atom that is within a predetermined distance range from the target to be evaluated among atoms that are different from the target to be evaluated in the system, and in the training, a three-dimensional wave function of each u-th atomic orbit from the orbit having the maximum occupancy level is obtained. The estimation model is updated such that the difference between the sum of the results obtained by executing the estimation model on each atom in the system and the energy of the system estimated by the density functional theory for the system is reduced.

The invention discloses a method for predicting Se vacancy infrared nonlinear optical response in few-layer PtSe2, and the method comprises the following steps: S1, constructing a few-layer PtSe2 model, and carrying out the in-plane expansion; s2, introducing Se vacancy into the model after cell expansion; s3, performing structural relaxation on the Se-containing vacancy structure until atomic force and energy converge; s4, calculating an electron energy band structure of the relaxed structure based on a density functional theory; s5, constructing a maximum localization Wannier function, and fitting the electron energy band structure obtained in the step S4; s6, calculating an in-plane nonlinear displacement current component of the few-layer PtSe2 in an infrared band; and S7, analyzing the change of the nonlinear displacement current component obtained in the step S6 in the infrared band, and predicting the influence of Se vacancy on the infrared nonlinear optical property. The infrared nonlinear optical property prediction method provided by the invention is low in cost and high in precision.

The invention provides a superconducting transition temperature detection system and method, relates to the technical field of superconducting transition temperature measurement, and solves the technical problems that in the prior art, superconducting transition temperature detection needs manual intervention, and the automation level is low. The system specifically comprises a first module, a second module and a third module which are sequentially cascaded, the first module is used for generating an input file which is adaptive to the superconducting property calculation step and has consistent parameters; the superconducting property calculation step comprises structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation and superconducting density functional theory solving; the second module is used for calculating and outputting the electroacoustic coupling matrix and coulomb interaction data based on the input file; and the third module is used for calling the SCDFT solver based on the electroacoustic coupling matrix and coulomb interaction data, outputting the superconducting transition temperature and generating a Fermi surface energy gap visualization file. The method is used for measuring the superconducting transition temperature.

The application relates to the field of environmental science and technology, and discloses a fine-source analysis method for atmospheric pollutants combining computational chemistry and isotopes, which comprises the following steps: constructing an isotopic data set, simulating a target pollutant molecular configuration, constructing an isotopic fractionation paradigm equation, correcting local isotopic fractionation, tracing the generation mechanism of a target pollutant in a typical region, and optimizing source analysis of the target pollutant in the typical region. In this way, based on computational chemistry, the density functional theory and the U-B-GM model are coupled, the isotopic fractionation paradigm equation is constructed for a preset reaction path, the specific isotopic fractionation value or fractionation coefficient is accurately simulated in combination with atmospheric environmental parameters during observation, and a local isotopic fractionation correction scheme is established accordingly; the correction scheme is oriented towards fine-source analysis, and can accurately identify the generation mechanism and pollution sources. In this way, the uncertainty of source analysis can be effectively reduced, and fine tracing of the sources of atmospheric pollutants can be realized.